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Issues And Trends In Healthcare Delivery System01:29

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Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases.

Manisha Panjeta1, Aryan Reddy2, Rushabh Shah2

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering Technology, Punjab, 147004 India.

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Summary
This summary is machine-generated.

Machine Learning (ML) and Deep Learning (DL) models offer flexible solutions for COVID-19 detection. This study systematically reviews ML and DL methods, evaluating their pros, cons, and effectiveness for pandemic response.

Keywords:
Artificial intelligenceConvolutional neural networkCovid-19Deep learningMachine learning

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Computational Biology

Background:

  • The healthcare system is increasingly leveraging Machine Learning (ML) and Deep Learning (DL) for improved diagnostics and operational efficiency.
  • The COVID-19 pandemic highlighted the strain on global healthcare infrastructure and the potential for AI to support overburdened systems.
  • AI technologies, particularly ML and DL, demonstrate adaptability crucial for addressing evolving public health challenges like pandemics.

Purpose of the Study:

  • To systematically review and analyze the application of ML and DL models in COVID-19 detection.
  • To evaluate the advantages and disadvantages of various ML and DL approaches for identifying COVID-19.
  • To provide a comparative assessment of different COVID-19 detection techniques based on key performance indicators.

Main Methods:

  • Systematic literature review of ML and DL models applied to COVID-19 detection.
  • Comparative analysis of detection methods based on availability, usability, accuracy, and cost.
  • Visual representation of the performance of different detection techniques.
  • Discussion of challenges and future research directions for integrating AI in diagnostics.

Main Results:

  • Comprehensive overview of diverse ML and DL-based COVID-19 detection strategies.
  • Comparative evaluation of methods highlighting strengths and weaknesses.
  • Visualizations illustrating the performance metrics of various detection techniques.
  • Identification of key factors influencing the practical implementation of AI in pandemic detection.

Conclusions:

  • ML and DL models present viable and adaptable tools for COVID-19 detection, offering significant potential to enhance healthcare responses.
  • A systematic evaluation framework is essential for understanding the trade-offs between different AI detection methods.
  • Further research is needed to address implementation challenges and optimize the integration of AI with existing diagnostic workflows.